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. 2025 Jul 30:16:1630032.
doi: 10.3389/fphys.2025.1630032. eCollection 2025.

Pulse rate variability is not the same as heart rate variability: findings from a large, diverse clinical population study

Allen B Kantrowitz #  1 , Kfir Ben-David #  2   3 , Michael Morris  4   5 , Harrison L Wittels  6   7 , Michael J Wishon  6 , Samantha M McDonald  6   8 , Eric J Renaghan  9 , Luis A Feigenbaum  10 , S Howard Wittels  6   11   12   13
Affiliations

Pulse rate variability is not the same as heart rate variability: findings from a large, diverse clinical population study

Allen B Kantrowitz et al. Front Physiol. .

Abstract

Introduction: Scientists and consumer products are increasingly employing light-based photoplethysmography (PPG) instead of electrocardiography (ECG) assuming it accurately quantifies heart rate variability (HRV). Recent studies, however, have demonstrated that pulse rate variability (PRV) derived from PPG is not equivalent to HRV-derived from ECG. This study investigated the agreement between PPG-PRV and ECG-HRV in a beat-to-beat analysis in 931 adults recruited from a tertiary academic medical center in the southeastern United States.

Methods: Participants wore two (chest and bicep) Warfighter Monitor™ devices (Tiger Tech Solutions, Inc.). Heart rate (HR), pulse rate (PR) and three time-domain indices for PPG-PRV and ECG-HRV were measured. ECG-derived RR and noise-filtered NN intervals were extracted to compute HR, SDNN (standard deviation of NN intervals), rMSSD (root mean square of successive differences), and pNN50 (percentage of successive NN intervals differing by >50 ms). PPG-derived pulse-wave peaks were detected to calculate corresponding PR/PRV metrics. Pearson correlation, Bland-Altman, and one-way ANOVA analyses assessed linear association, bias, and mean differences across select chronic diseases.

Results: Significant disagreement and differences were observed between ECG-HRV and PPG-PRV (p < 0.001 for all). For rMSSD: cardiovascular: 3.04 ms, 95% CI: 1.33, 4.75, endocrine: 2.85 ms, 95% CI: 0.52, 5.18, and neurological: 4.39 ms, 95% CI: 1.39, 7.39). For SDNN: cardiovascular: 8.50 ms, 95% CI: 5.25, 11.74, endocrine: 8.43 ms, 95% CI: 3.97, 12.90, neurological: 11.84 ms, 95% CI: 6.02, 17.67, and respiratory: 7.23 ms, 95% CI: 1.83, 12.62). For pNN50: cardiovascular: 2.48%, 95% CI: 1.67, 3.3, endocrine: 2.21% 95% CI: 1.12, 3.29, neurological: 2.91%, 95% CI: 1.25, 4.32, and respiratory: 1.46%, 95% CI: 0.15, 2.77).

Discussion: PPG-PRV is a poor surrogate for ECG- HRV as it significantly underestimated SDNN, rMSSD, and pNN50 across select chronic diseases. Given the widespread use of PPG-based devices and ubiquitous, incorrect assumption that PRV accurately reflects HRV, researchers, clinicians, and manufacturers must clearly distinguish between PRV and HRV in studies and product claims.

Keywords: autonomic nervous system; cardiovascular; electrocardiogram; heart rate variability; pulse rate variability; pulse wave; technology; wearable.

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Conflict of interest statement

Authors HW, MW, SM, and SW were employed by Tiger Tech Solutions, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Bland Altman Plots Evaluating Agreement Between ECG and PPG Methods in Measuring HRV Metrics. [(A), top row, left] HR: ECG chest vs. ECG bicep [(B), top row, right] HR: ECG chest/bicep vs. PPG bicep, [(C), 2nd row, left] rMSSD: ECG chest vs. ECG bicep, [(D), 2nd row, right] rMSSD: ECG chest/bicep vs. PPG bicep, [(E), 3rd row, left] SDNN: ECG chest vs. ECG bicep, [(F), 3rd row, right] SDNN: ECG chest/bicep vs. PPG bicep, [(G), 4th row, left] pNN50: ECG chest vs. ECG bicep and [(H), 4th row, right] pNN50: ECG chest/bicep vs. PPG bicep.
FIGURE 2
FIGURE 2
Pearson Correlations Evaluating the Relationships Between ECG and PPG Methods in Measuring HRV Metrics. [(A), top row, left] HR: ECG chest vs. ECG bicep [(B), top row, right] HR: ECG chest/bicep vs. PPG bicep, [(C), 2nd row, left] rMSSD: ECG chest vs. ECG bicep, [(D), 2nd row, right] rMSSD: ECG chest/bicep vs. PPG bicep, [(E), 3rd row, left] SDNN: ECG chest vs. ECG bicep, [(F), 3rd row, right] SDNN: ECG chest/bicep vs. PPG bicep, [(G), 4th row, left] pNN50: ECG chest vs. ECG bicep and [(H), 4th row, right] pNN50: ECG chest/bicep vs. PPG bicep.
FIGURE 3
FIGURE 3
Histogram Plots for SDNN (ms) Measured by ECG and PPG Across Several Common Chronic Diseases. ECG location–chest and bicep. PPG location–bicep.
FIGURE 4
FIGURE 4
Histogram Plots for rMSSD (ms) Measured by ECG and PPG Across Several Common Chronic Diseases. ECG location–chest and bicep. PPG location–bicep.
FIGURE 5
FIGURE 5
Histogram Plots for pNN50 (%) Measured by ECG and PPG Across Several Common Chronic Diseases. ECG location–chest and bicep. PPG location–bicep.
FIGURE 6
FIGURE 6
ECG and PPG Signal Output from the Warfighter Monitor™ over a 10-s interval.
See this image and copyright information in PMC

References

    1. Ajdaraga E., Gusev M. (2017). Analysis of sampling frequency and resolution in ECG signals. In: 2017 25th telecommunication forum (TELFOR); 2017 November 21–22; Belgrade, Serbia: IEEE Xplore. p. 1–4. 10.1109/TELFOR.2017.8249438 - DOI
    1. Ajmal, Boonya-Ananta T., Rodriguez A. J., Du Le V. N., Ramella-Roman J. C. (2021). Monte carlo analysis of optical heart rate sensors in commercial wearables: the effect of skin tone and obesity on the photoplethysmography (PPG) signal. Biomed. Opt. Express 12, 7445–7457. 10.1364/BOE.439893 - DOI - PMC - PubMed
    1. Akinrinmade A. O., Adebile T. M., Ezuma-Ebong C., Bolaji K., Ajufo A., Adigun A. O., et al. (2023). Artificial intelligence in healthcare: perception and reality. Cureus 15, e45594. 10.7759/cureus.45594 - DOI - PMC - PubMed
    1. Allen J. (2007). Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28, R1–R39. 10.1088/0967-3334/28/3/R01 - DOI - PubMed
    1. Alowais S. A., Alghamdi S. S., Alsuhebany N., Alqahtani T., Alshaya A. I., Almohareb S. N., et al. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med. Educ. 23, 689. 10.1186/s12909-023-04698-z - DOI - PMC - PubMed

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